SoroushVahidi/scidocs huggingface dataset:
A framework for thermal building parameter identification and simulation
(2016)

Studies related to Smart Energy technologies have been highlighted in the last years, specially by significantly contributing to the saving of energy consumption, to the reduction of CO2 emissions and to the diminishing of the paid values in electricity bills. The electrical-thermal analogy can be used in order to simulate heating behavior in real situations and it also may be considered a contributor to Smart Energy technologies. This is the reason why several simulation tools are used. Taking this into account, this paper presents a framework using NGSpice simulator to identify both, the thermal capacitance and the thermal resistance parameters from a hypothetical monitored room which in a Smart Building context. The results from the proposed framework are compared to OpenModelica software and to the contributions from literature.
AXIAL-FLUX PERMANENT-MAGNET MOTOR DESIGN FOR ELECTRIC VEHICLE DIRECT DRIVE USING SIZING EQUATION AND FINITE ELEMENT ANALYSIS
(2011)

The design process of a double-sided slotted TORUS axialflux permanent-magnet (AFPM) motor suitable for direct drive of electric vehicle (EV) is presented. It used sizing equation and Finite Element Analysis (FEA). AFPM motor is a high-torque-density motor easily mounted compactly onto a vehicle wheel, fitting the wheel rim perfectly. A preliminary design is a double-sided slotted AFPM motor with 6 rotor poles for high torque-density and stable rotation. In determining the design requirements, a simple vehicle-dynamics model that evaluates vehicle performance through the typical cruising trip of an automobile was considered. To obtain, with the highest possible torque, the initial design parameters of the motor, AFPM’s fundamental theory and sizing equation were applied. Vector Field Opera-3D 14.0 commercial software ran the FEA of the motor design, evaluating and enhancing accuracy of the design parameters. Results of the FEA simulation were compared with those obtained from the sizing equation; at no-load condition, the flux density at every part of the motor agreed. The motor’s design meets all the requirements and limits of EV, and fits the shape and size of a classical-vehicle wheel rim. The design process is comprehensive and can be used for an arbitrary EV with an arbitrary cruising scenario.
Hybrid context-sensitivity for points-to analysis
(2013)

Context-sensitive points-to analysis is valuable for achieving high precision with good performance. The standard flavors of context-sensitivity are call-site-sensitivity (kCFA) and object-sensitivity. Combining both flavors of context-sensitivity increases precision but at an infeasibly high cost. We show that a selective combination of call-site- and object-sensitivity for Java points-to analysis is highly profitable. Namely, by keeping a combined context only when analyzing selected language features, we can closely approximate the precision of an analysis that keeps both contexts at all times. In terms of speed, the selective combination of both kinds of context not only vastly outperforms non-selective combinations but is also faster than a mere object-sensitive analysis. This result holds for a large array of analyses (e.g., 1-object-sensitive, 2-object-sensitive with a context-sensitive heap, type-sensitive) establishing a new set of performance/precision sweet spots.
Probabilistic neural networks
(1990)

-By replacing the sigmoid activation function often used in neural networks with an exponential function, a probabilistic neural network ( PNN) that can compute nonlinear decision boundaries which approach the Bayes optimal is formed. Alternate activation functions having similar properties are also discussed. A fourlayer neural network of the type proposed can map any input pattern to any number of classifications. The decision boundaries can be modified in real-time using new data as they become available, and can be implemented using artificial hardware "'neurons" that operate entirely in parallel. Provision is also made for estimating the probability and reliability of a classification as well as making the decision. The technique offers a tremendous speed advantage for problems in which the incremental adaptation time of back propagation is a significant fraction of the total computation time. For one application, the PNN paradigm was 200,000 times faster than back-propagation. Keywords--Neural network, Probability density function, Parallel processor, "Neuron", Pattern recognition, Parzen window, Bayes strategy, Associative memory.
Multiband antenna for Bluetooth/ZigBee/ Wi-Fi/WiMAX/WLAN/X-band applications: Partial ground with periodic structures and EBG
(2016)

A compact in size microstrip antenna with multiband resonance features is described in this paper. A circular antenna with the partial ground is used as a primary antenna which resonated for WiMAX band (3.3–3.7 GHz). Further, primary antenna embedded with mushroom shape EBG (Electromagnetic Band Gap) and periodic DGS (Defected Ground Structure) to operate over WLAN band (5.1–5.8 GHz) and X-band from 8–12 GHz. Proposed antenna covers 2.4/3.5/5.5 GHz bands and complete X-band which also reflects that it has novel suitability to use for a ZigBee/Wi-Fi/Bluetooth applications. The proposed antenna is etched on FR-4 substrate and overall dimension is 20×26×1.6 mm3. The projected antenna has wide-ranging bandwidth with VSWR < 2. It provides favorable agreement between measured and simulated results.
Philosophy in the Flesh : The Embodied Mind and its Challenge to Western Thought
(2002)

Part I supports these sentences with findings from cognitive science "that human reason is a form of animal reason, a reason inextricably tied to our bodies and the peculiarities of our brains" and "that our bodies, brains, and interactions with our environment provide the mostly unconscious basis for our everyday metaphysics, that is, our sense of what is real." The third sentence is a summary of their earlier work on metaphor (Lakoff and Johnson 1980), which in Part II of this book they extend to a more detailed analysis of the metaphors underlying basic philosophical issues, such as time, events and causes, mind, self, and morality. Part III applies that analysis to the metaphors tacitly assumed by philosophers ranging from the Presocratics to Noam Chomsky. Part IV presents arguments for "empirically responsible philosophy" and its potential for understanding "who we are, how we experience our world, and how we ought to live." Finally, the appendix summarizes research inspired by this philosophy that has produced computational simulations of certain aspects of embodied minds. Given such a broad scope, the authors have not been able to cover all the topics with equal success. Instead of challenging all of western philosophy, they should have concentrated on their major opponent, Noam Chomsky and his philosophy of language. Lakoff began his career in linguistics as a student and later a teacher of Chomsky's version of transformational grammar. But in the late 1960s, he joined with other former students to promote generative semantics as an alternative to Chomsky's generative syntax. The result was a series of "linguistic wars," whose history has been retold by various participants over the past twenty years. Chapter 22 of this book presents a strong case against Chomsky's "autonomous syntax" and for an approach that bases syntax on semantics and semantics on the bodily mechanisms of perception and action. The philosophical and linguistic arguments of Chapter 22 are reinforced in the appendix by a summary of the Neural Theory of Language (NTL), which is being developed by Jerome Feldman, George Lakoff, Lokendra Shastri, and their students. To date, the NTL group has undertaken three major tasks in language understanding
A VoIP emergency services architecture and prototype
(2005)

Providing emergency services in VoIP networks are vital to the success of VoIP. It not only presents design and implementation challenges, but also gives an opportunity to enhance the existing emergency call handling infrastructure. We propose an architecture to deliver emergency services in SIP-based VoIP networks, which can accommodate PSTN calls through PSTN to SIP gateways. Our architecture addresses the issues of identifying emergency calls, determining callers' locations, routing emergency calls to appropriate public safety answering points (PSAPs), and presenting required information to emergency call takers. We have developed a prototype implementation to prove our architecture's feasibility and scalability. We expect to undertake a pilot project at a working PSAP with our implementation once it is thoroughly tested.
Big data analytics on traditional HPC infrastructure using two-level storage
(2015)

Data-intensive computing has become one of the major workloads on traditional high-performance computing (HPC) clusters. Currently, deploying data-intensive computing software framework on HPC clusters still faces performance and scalability issues. In this paper, we develop a new two-level storage system by integrating Tachyon, an in-memory file system with OrangeFS, a parallel file system. We model the I/O throughputs of four storage structures: HDFS, OrangeFS, Tachyon and two-level storage. We conduct computational experiments to characterize I/O throughput behavior of two-level storage and compare its performance to that of HDFS and OrangeFS, using TeraSort benchmark. Theoretical models and experimental tests both show that the two-level storage system can increase the aggregate I/O throughputs. This work lays a solid foundation for future work in designing and building HPC systems that can provide a better support on I/O intensive workloads with preserving existing computing resources.
Sharp Finite-Time Iterated-Logarithm Martingale Concentration
(2014)

We give concentration bounds for martingales that are uniform over finite times and extend classical Hoeffding and Bernstein inequalities. We also demonstrate our concentration bounds to be optimal with a matching anti-concentration inequality, proved using the same method. Together these constitute a finite-time version of the law of the iterated logarithm, and shed light on the relationship between it and the central limit theorem.
Finite-time Analysis of the Multiarmed Bandit Problem
(2002)

Reinforcement learning policies face the exploration versus exploitation dilemma, i.e. the search for a balance between exploring the environment to find profitable actions while taking the empirically best action as often as possible. A popular measure of a policy's success in addressing this dilemma is the regret, that is the loss due to the fact that the globally optimal policy is not followed all the times. One of the simplest examples of the exploration/exploitation dilemma is the multi-armed bandit problem. Lai and Robbins were the first ones to show that the regret for this problem has to grow at least logarithmically in the number of plays. Since then, policies which asymptotically achieve this regret have been devised by Lai and Robbins and many others. In this work we show that the optimal logarithmic regret is also achievable uniformly over time, with simple and efficient policies, and for all reward distributions with bounded support.
Achieving Privacy in the Adversarial Multi-Armed Bandit
(2017)

In this paper, we improve the previously best known regret bound to achieve -differential privacy in oblivious adversarial bandits from O(T / ) to O( √ T lnT/ ). This is achieved by combining a Laplace Mechanism with EXP3. We show that though EXP3 is already differentially private, it leaks a linear amount of information in T . However, we can improve this privacy by relying on its intrinsic exponential mechanism for selecting actions. This allows us to reach O( √ lnT )-DP, with a regret of O(T ) that holds against an adaptive adversary, an improvement from the best known of O(T ). This is done by using an algorithm that run EXP3 in a mini-batch loop. Finally, we run experiments that clearly demonstrate the validity of our theoretical analysis.
Further Optimal Regret Bounds for Thompson Sampling
(2013)

Thompson Sampling is one of the oldest heuristics for multi-armed bandit problems. It is a randomized algorithm based on Bayesian ideas, and has recently generated significant interest after several studies demonstrated it to have comparable or better empirical performance compared to the state of the art methods. In this paper, we provide a novel regret analysis for Thompson Sampling that proves the first near-optimal problem-independent bound of O( √ NT lnT ) on the expected regret of this algorithm. Our novel martingalebased analysis techniques are conceptually simple, and easily extend to distributions other than the Beta distribution. For the version of Thompson Sampling that uses Gaussian priors, we prove a problemindependent bound of O( √ NT lnN) on the expected regret, and demonstrate the optimality of this bound by providing a matching lower bound. This lower bound of Ω( √ NT lnN) is the first lower bound on the performance of a natural version of Thompson Sampling that is away from the optimal bound (O( √ NT )) achievable for the multi-armed bandit problem by another algorithm [4]. Our near-optimal problem-independent bounds for Thompson Sampling solve a COLT 2012 open problem of Chapelle and Li. Additionally, our techniques simultaneously provide the optimal problem-dependent bound of (1 + ) ∑ i lnT d(μi,μ1) +O( 2 ) on the expected regret. The optimal problem-dependent regret bound for this problem was first Preliminary work. Under review by AISTATS 2013. Do not distribute. proven recently by Kaufmann et al. [15].
Adaptation in natural and artificial systems
(1992)

Name of founding work in the area. Adaptation is key to survival and evolution. Evolution implicitly optimizes organisims. AI wants to mimic biological optimization { Survival of the ttest { Exploration and exploitation { Niche nding { Robust across changing environments (Mammals v. Dinos) { Self-regulation,-repair and-reproduction 2 Artiicial Inteligence Some deenitions { "Making computers do what they do in the movies" { "Making computers do what humans (currently) do best" { "Giving computers common sense; letting them make simple deci-sions" (do as I want, not what I say) { "Anything too new to be pidgeonholed" Adaptation and modiication is root of intelligence Some (Non-GA) branches of AI: { Expert Systems (Rule based deduction)
Best of both worlds: Human-machine collaboration for object annotation
(2015)

The long-standing goal of localizing every object in an image remains elusive. Manually annotating objects is quite expensive despite crowd engineering innovations. Current state-of-the-art automatic object detectors can accurately detect at most a few objects per image. This paper brings together the latest advancements in object detection and in crowd engineering into a principled framework for accurately and efficiently localizing objects in images. The input to the system is an image to annotate and a set of annotation constraints: desired precision, utility and/or human cost of the labeling. The output is a set of object annotations, informed by human feedback and computer vision. Our model seamlessly integrates multiple computer vision models with multiple sources of human input in a Markov Decision Process. We empirically validate the effectiveness of our human-in-the-loop labeling approach on the ILSVRC2014 object detection dataset.
Doc 2 Cube : Automated Document Allocation to Text Cube via Dimension-Aware Joint Embedding
(2018)

Data cube is a cornerstone architecture in multidimensional analysis of structured datasets. It is highly desirable to conduct multidimensional analysis on text corpora with cube structures for various text-intensive applications in healthcare, business intelligence, and social media analysis. However, one bottleneck to constructing text cube is to automatically put millions of documents into the right cells in such a text cube so that quality multidimensional analysis can be conducted afterwards—it is too expensive to allocate documents manually or rely on massively labeled data. We propose Doc2Cube, a method that constructs a text cube from a given text corpus in an unsupervised way. Initially, only the label names (e.g., USA, China) of each dimension (e.g., location) are provided instead of any labeled data. Doc2Cube leverages label names as weak supervision signals and iteratively performs joint embedding of labels, terms, and documents to uncover their semantic similarities. To generate joint embeddings that are discriminative for cube construction, Doc2Cube learns dimension-tailored document representations by selectively focusing on terms that are highly label-indicative in each dimension. Furthermore, Doc2Cube alleviates label sparsity by propagating the information from label names to other terms and enriching the labeled term set. Our experiments on a real news corpus demonstrate that Doc2Cube outperforms existing methods significantly. Doc2Cube is a technology transferred to U.S. Army Research Lab and is a core component of the EventCube system that is being deployed for multidimensional news and social media data analysis.
User behaviour analysis in context-aware recommender system using hybrid filtering approach
(2013)

The incorporation of context in recommendation systems is important for providing good recommendations as it improves the performance. Context awareness is an important concept in many disciplines. There are various methods that have already been proposed for context aware recommendation systems and can be classified into pre-filter, post-filter and contextual generalization. However, we found that there are some problems in using a single approach. So, in this paper we propose a new approach by combining pre-filter and post-filter methods based on the importance of contextual attribute. We believe that the contextual attribute should be dynamically selected for every user, as we found that it varies according to the user behaviour. In our approach, in case of a few rating scenario a simple method is used to provide a solution. The strategy adopted is advantageous over the single approach and is effective in improving the accuracy for the context aware recommender system.
Extracting implicit features in online customer reviews for opinion mining
(2013)

As the number of customer reviews grows very rapidly, it is essential to summarize useful opinions for buyers, sellers and producers. One key step of opinion mining is feature extraction. Most existing research focus on finding explicit features, only a few attempts have been made to extract implicit features. Nearly all existing research only concentrate on product features, few has paid attention to other features that relates to sellers, services and logistics. Therefore in this paper, we propose a novel co-occurrence association-based method, which aims to extract implicit features in customer reviews and provide more comprehensive and fine-grained mining results.
A 65nm logic technology featuring 35nm gate lengths, enhanced channel strain, 8 Cu interconnect layers, low-k ILD and 0.57 /spl mu/m/sup 2/ SRAM cell
(2004)

A 65nm generation logic technology with 1.2nm physical gate oxide, 35nm gate length, enhanced channel strain, NiSi, 8 layers of Cu interconnect, and low-k ILD for dense high performance logic is presented. Transistor gate length is scaled down to 35nm while not scaling the gate oxide as a means to improve performance and reduce power. Increased NMOS and PMOS drive currents are achieved by enhanced channel strain and junction engineering. 193nm lithography along with APSM mask technology is used on critical layers to provide aggressive design rules and a 6-T SRAM cell size of 0.57/spl mu/m/sup 2/. Process yield, performance and reliability are demonstrated on a 70 Mbit SRAM test vehicle with >0.5 billion transistors.
Multiple String LED Driver With Flexible and High-Performance PWM Dimming Control
(2017)

The main objectives in driving multiple LED strings include achieving uniform current control and high-performance pulse width modulation (PWM) dimming for all strings. This study proposes a new multiple string LED driver to achieve not only current balance, but also flexible and wide range PWM dimming ratio for each string. A compact single-inductor multiple-output topology is adopted in the driver, accompanied by synchronous integrators and variable dimming frequency, to achieve both high-efficiency and high-performance dimming. By using the proposed variable dimming frequency scheme, high dimming frequency is applied to a string with high dimming ratio, which helps us to maintain the deviation of LED string current in an acceptable range, while low dimming frequency is applied to a string with low dimming ratio, which helps us to achieve rectangular LED current waveform. Meanwhile, the new time multiplexing control scheme automatically optimizes the LED strings’ bus voltages, thus minimizes each string's power loss. A three-string LED driver prototype is constructed to validate the effectiveness of the proposed control scheme, where the three strings can have different dimming ratios between 4% and 100%.
Reinforcement Learning Design-Based Adaptive Tracking Control With Less Learning Parameters for Nonlinear Discrete-Time MIMO Systems
(2015)

Based on the neural network (NN) approximator, an online reinforcement learning algorithm is proposed for a class of affine multiple input and multiple output (MIMO) nonlinear discrete-time systems with unknown functions and disturbances. In the design procedure, two networks are provided where one is an action network to generate an optimal control signal and the other is a critic network to approximate the cost function. An optimal control signal and adaptation laws can be generated based on two NNs. In the previous approaches, the weights of critic and action networks are updated based on the gradient descent rule and the estimations of optimal weight vectors are directly adjusted in the design. Consequently, compared with the existing results, the main contributions of this paper are: (1) only two parameters are needed to be adjusted, and thus the number of the adaptation laws is smaller than the previous results and (2) the updating parameters do not depend on the number of the subsystems for MIMO systems and the tuning rules are replaced by adjusting the norms on optimal weight vectors in both action and critic networks. It is proven that the tracking errors, the adaptation laws, and the control inputs are uniformly bounded using Lyapunov analysis method. The simulation examples are employed to illustrate the effectiveness of the proposed algorithm.
Personalized Web Search in Web Mining
(2015)

Web is a collection of inter-related files on one or more Web servers. Web mining is the application of data mining techniques to extract knowledge from Web data. Web mining involves the process to apply data mining techniques to extract and uncover knowledge from web documents and services. Web mining has been explored to a vast level and different techniques have been defined for variety of applications like Web Search, Web Classification and Personalization etc. Personalized search is a way to improve the accuracy of web search. However personalized search requires collection and aggregation of user information, which often raise serious problems of breach of confidential data for many users. So, personalized web search in web mining is one of the techniques to improve privacy concerns of the user’s data.